Article
Automation & Control Systems
Huanqing Wang, Shijia Kang, Xudong Zhao, Ning Xu, Tieshan Li
Summary: This article presents an adaptive neural-network command-filtered tracking control scheme for nonlinear systems with multiple actuator constraints. The use of an equivalent transformation method addresses the issue of actuator nonlinearity, while the command filter method helps solve the complexity problem. The proposed control strategy, utilizing neural-network approximation and backstepping design, ensures boundedness of variables and limited output tracking error fluctuation near the origin. Simulation results confirm the effectiveness of the designed control strategy.
IEEE TRANSACTIONS ON CYBERNETICS
(2022)
Article
Computer Science, Artificial Intelligence
Jiapeng Liu, Qing-Guo Wang, Jinpeng Yu
Summary: This paper presents a modified event-triggered command filter backstepping tracking control scheme for a class of uncertain nonlinear systems with unknown input saturation. The scheme addresses uncertainties in subsystems by using command filters to reconstruct virtual control functions, and employs a piecewise continuous function to deal with the unknown input saturation problem. An event-triggered tracking controller is developed using adaptive neural network technique. Simulation studies validate the effectiveness of the controller.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Fabin Cheng, Huanqing Wang, Guangdeng Zong, Ben Niu, Xudong Zhao
Summary: This article considers the problem of finite-time command-filtered control for switched nonlinear systems with input quantization and output constraints. The unmeasurable state is estimated by designing a switched state observer. Hysteresis quantization and barrier Lyapunov function approach are used to solve the chattering problem and restrict the output to an expected range. A first-order Levant differentiator is used to accurately filter the intermediate signals and ensure the finite-time stability of the filter. Stability of the closed-loop system is proved using multiple Lyapunov functions.
CIRCUITS SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Lijun Long, Fenglan Wang
Summary: This article presents a dynamic event-triggered adaptive neural network control approach for switched nonlinear systems. By utilizing switched command filter and common Lyapunov function method, the issues of asynchronous switching and discontinuous measurement error are addressed. Numerical examples demonstrate the effectiveness of the proposed approach.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Information Systems
Licheng Zheng, Zhi Liu, C. L. Philip Chen, Yun Zhang
Summary: This paper presents an adaptive neural control strategy for uncertain nonlinear switched systems with dwell time, addressing unknown nonlinear functions, hysteresis nonlinearities and external disturbances. By introducing a filter-contained hysteretic quantizer, the paper offers a durable solution for conflicts between hysteresis actuation and quantization, ensuring that tracking errors converge to an adjustable range near zero.
INFORMATION SCIENCES
(2021)
Article
Automation & Control Systems
Xueling Li, Xiangze Lin, Yun Zou
Summary: This paper presents a smooth output feedback control method to globally stabilize planar switched nonlinear systems with asymmetric output constraints. The method is applicable to various switching scenarios and has been validated through simulations.
NONLINEAR ANALYSIS-HYBRID SYSTEMS
(2021)
Article
Automation & Control Systems
Qitian Yin, Hongyang Zhang, Quanqi Mu, Jianbai Yang, Qinghua Ma
Summary: This study presents an output backstepping control architecture based on command filter and Multilayer-Neural-Network Pre-Observer to achieve reference signal tracking of arbitrarily switching nonlinear systems. The proposed approach compensates for the chattering caused by the switching parameter and guarantees bounded states of the closed-loop system. The developed backstepping control method combines servo reconstruction and control, resulting in improved tracking performance.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Automation & Control Systems
Tianping Zhang, Tao Liu
Summary: This article proposes an optimal command-filtered backstepping control approach for uncertain strict-feedback nonlinear multi-agent systems with output constraints and unmodeled dynamics. By using one-to-one nonlinear mapping and dynamical signals, the constrained systems are recast as corresponding unrestricted systems and cope with unmodeled dynamics. The feedforward and feedback controllers are designed based on dynamic surface control, adaptive dynamic programming, and integral reinforcement learning techniques. The simulation example demonstrates the feasibility of the proposed control algorithm in achieving cooperative semi-globally uniformly ultimately bounded signals and maintaining output constraints.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Computer Science, Information Systems
Qingkun Yu, Xiqin He, Libing Wu, Liangdong Guo
Summary: This paper addresses the design of neural network observer and adaptive finite-time tracking controller for uncertain nonlinear systems with event-triggered inputs and unknown dead-zone constraints. By improving the finite-time command filter backstepping technique and developing an adaptive output feedback event triggering mechanism, the goal of finite-time convergence is achieved and network bandwidth is effectively saved.
INFORMATION SCIENCES
(2022)
Article
Mathematics, Applied
Yuanqing Wang, Ning Xu, Yajuan Liu, Xudong Zhao
Summary: An adaptive neural fault-tolerant control strategy for a class of switched nonlinear systems subject to actuator fault is proposed in this article, using the command filter approach to approximate unknown nonlinear functions and estimate unmeasurable states. By applying the backstepping algorithm and average dwell time method, an adaptive NNs fault tolerant controller is developed to ensure signal boundedness within the system.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Automation & Control Systems
Shi Li, Choon Ki Ahn, Jian Guo, Zhengrong Xiang
Summary: This article investigates the sampled-data stabilization problem of a class of switched nonlinear systems using radial basis function neural networks to relax restrictions on unknown nonlinear functions. Novel mode-dependent adaptive laws and sampled-data control laws are constructed to avoid Zeno behavior, and a new allowable sampling period is deduced to guarantee bounded states of the closed-loop system (CLS). The proposed method's effectiveness is demonstrated through two examples.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Automation & Control Systems
Zhengbao Cao
Summary: This article studies the QSR-dissipativity of feedback interconnection of switched nonlinear systems via event-triggered control, and proposes a control scheme to ensure the QSR-dissipativity of the systems. Zeno behavior is excluded and the maximum number of triggers is estimated.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Ke Xu, Huanqing Wang, Qiang Zhang, Ming Chen, Junfei Qiao, Ben Niu
Summary: The paper investigates the command-filter-based adaptive tracking control for a class of stochastic nonlinear systems with strict-feedback structure with input dead-zone. By introducing the control method of the command-filter and combining adaptive backstepping design algorithm and Lyapunov stability theorem, an adaptive neural command-filter controller is developed, ensuring closed-loop signals stability and tracking error convergence.
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
(2021)
Article
Automation & Control Systems
Jun Guo, Yuming Bo, Ju H. Park, Jiali Ma
Summary: This article investigates command filter-based adaptive fault-tolerant control for a class of nonlinear systems dealing with unknown control directions and disturbance. It uses neural networks to handle nonlinear functions, command filters to handle complexity issues, and bound estimation methods and Nussbaum functions to compensate for actuator faults and unknown directions. The proposed method ensures tracking errors converge to bounded compact sets and all closed-loop signals are bounded, as demonstrated in three simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2022)
Article
Computer Science, Information Systems
Yan Yan, Xiqin He, Libing Wu, Qingkun Yu
Summary: This article focuses on adaptive event-triggered control (ETC) for a family of switched nonlinear systems with full-state constraints. A first-order command filter is introduced to reduce complexity, and compensating signals are designed to cancel filtering errors. By combining the filtered control and backstepping technology, a new event-triggered controller based on fuzzy logic systems (FLSs) is proposed, which reduces triggering frequency and handles mismatch problems. The control scheme based on barrier Lyapunov functions (BLFs) guarantees no violation of full-state constraints and convergence of tracking error to the origin's neighborhood. The effectiveness of the proposed control scheme is illustrated through two simulation examples.
INFORMATION SCIENCES
(2023)
Article
Automation & Control Systems
Yunjun Chen, Xin Mao, Guangdeng Zong, Dong Yang, Kaibo Shi
Summary: This paper investigates the event-triggered finite-time dynamic output feedback control for switched affine systems with asynchronous switching. The finite-time stability performance of the closed-loop systems is achieved by setting up a dynamic output feedback switched affine controller together with an event-triggered mechanism. The study discusses the system dynamics under the proposed mechanism during the synchronous and asynchronous switching intervals, emphasizing the existence of the lower bound on interevent intervals to exclude the occurrence of Zeno behavior.
ASIAN JOURNAL OF CONTROL
(2023)
Article
Computer Science, Theory & Methods
Guangdeng Zong, Xue Sun, Dong Yang, Shun-Feng Su, Kaibo Shi
Summary: This paper investigates the finite-time H-infinity control problem for switched fuzzy systems using the edge-dependent average dwell time switching. A dynamic event-triggered mechanism is adopted to alleviate communication pressure, and an adaptive law is set up to adjust the threshold on-line, which deeply affects the triggering times. It is rigorously proved that the Zeno behavior is excluded. A set of dynamic event-triggered controllers and edge-dependent average dwell time switching signals are co-designed to achieve desired performance. Finally, a chemical reaction example is given to validate the effectiveness of the proposed method.
FUZZY SETS AND SYSTEMS
(2023)
Article
Engineering, Electrical & Electronic
Xinjun Wang, Ben Niu, Huanqing Wang, Xudong Zhao, Wendi Chen
Summary: This article focuses on the adaptive bipartite consensus issue of nonlinear multi-agent systems in directed graphs from a new perspective. A new distributed control algorithm, named finite-time prescribed performance control, is designed by using a prescribed performance function and a novel first-order filter. This algorithm ensures that the bipartite consensus errors converge to a prescribed compact set within a finite time and allows the system to achieve the prescribed performance and fast finite-time convergence. Furthermore, neural networks are introduced to handle the continuous unknown nonlinearity and the effect of non-strict feedback structure in the system, while a dynamic surface control mechanism with a novel first-order filter is used to overcome the complexity explosion problem in controller design. Simulation experiments on forced damped pendulums are conducted to demonstrate the feasibility of the theoretical work.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS
(2023)
Article
Computer Science, Artificial Intelligence
Huanqing Wang, Jiawei Ma, Xudong Zhao, Ben Niu, Ming Chen, Wei Wang
Summary: This article considers the adaptive fuzzy fixed-time fault-tolerant tracking control problem for high-order nonlinear systems (HONSs) with sensor and actuator faults. Fuzzy logic systems are used to approximate the unknown nonlinear functions of the HONS. Based on backstepping technology and fixed-time theory, an adaptive fuzzy fixed-time fault-tolerant controller is developed to ensure bounded signals of the closed-loop HONS. A numerical example is presented to demonstrate the rationality of the proposed method.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2023)
Article
Automation & Control Systems
Haiyang Chen, Guangdeng Zong, Xudong Zhao, Fangzheng Gao, Kaibo Shi
Summary: This article investigates the finite-time secure filter design of fuzzy switched cyber-physical systems equipped with a resource-constraint network that may undergo false data injection attacks (FDIAs). A multidomain probabilistic event-triggered mechanism (MDPETM) is developed to strike a balance between resource consumption and filtering performance, and a delayed switching signal is used to characterize the mode mismatched phenomenon between the filter and the system. Fuzzy mismatched secure filters are then devised based on MDPETM and a virtual delay partitioning approach, and filter-mode-dependent Lyapunov functionals are created to achieve finite-time boundedness of the filtering error subject to admissible FDIAs.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Automation & Control Systems
Li-Min Han, Wei Su, Ben Niu, Xiao-Mei Wang, Xiao-Mei Liu
Summary: This paper proposes an adaptive compensation control algorithm to solve the actuator failures problem of nonlinear stochastic multi-agent systems under directed communication topology. Fuzzy logic systems are used to deal with the unknown nonlinearities, and a threshold-based event-triggered mechanism is considered to reduce communication burden. The dynamic surface control technique is also used to solve the issue of complexity explosion. Simulation results demonstrate the validity of the proposed design scheme.
IET CONTROL THEORY AND APPLICATIONS
(2023)
Article
Automation & Control Systems
Qian Xu, Guangdeng Zong, Yunjun Chen, Ben Niu, Kaibo Shi
Summary: This paper investigates the command filter-based adaptive neural network tracking control problem for uncertain nonsmooth nonlinear systems. An integral barrier Lyapunov function is introduced to handle the symmetric output constraint, and the Filippov's differential inclusion theory and approximation theorem are used to convert the nonsmooth nonlinear system to an equivalent smooth nonlinear system. Levant's differentiator is employed to handle the complexity explosion problem, and an error compensation mechanism is established to attenuate the effect of filtering error on control performance. By resorting to the backstepping technique, an adaptive neural network controller is set up. It is mathematically proved that the tracking error can converge to an arbitrarily small neighborhood of the origin, and all signals in the closed-loop system are semi-globally uniformly ultimately bounded. Numerical and application examples are provided to demonstrate the effectiveness of the proposed control strategy.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2023)
Article
Automation & Control Systems
Hao Jiang, Xiaomei Wang, Ben Niu, Huanqing Wang, Xinyu Liu
Summary: This article focuses on the event-triggered adaptive tracking containment control problem for a class of nonlinear multi-agent systems. To tackle the difficulties caused by unknown nonlinearities and unmodeled dynamics, Gaussian function properties and novel dynamics signals are used. A relative threshold-based event-triggered mechanism is also adopted to reduce system communication burden. The proposed protocol ensures convergence of followers' outputs to the convex hull spanned by the leaders' outputs, uniformly ultimate boundedness of all signals in the closed-loop system, and effective avoidance of Zeno behavior. Simulation results are provided to validate the effectiveness of the proposed containment control protocol.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Haiyang Chen, Fangzheng Gao, Guangdeng Zong
Summary: This article proposes a solution to the finite-time dissipative fuzzy state estimation problem for mixed cyber attacks. It reduces unwanted network traffic through a probabilistic event-triggered mechanism and tackles dual asynchronizations. By constructing Takagi-Sugeno fuzzy state estimators based on imperfect measurements, and establishing less conservative criteria, the strictly finite-time state estimation performance is achieved.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Wenhai Qi, Yakun Hou, Ju H. Park, Guangdeng Zong, Jinde Cao, Jun Cheng
Summary: This article focuses on the problem of discrete-time sliding mode control (DTSMC) for uncertain networked semi-Markovian switching systems (S-MSSs) under random denial-of-service (DoS) attacks. The semi-Markovian kernel (SMK) approach is used for controlling the switching between different modes, and a sliding mode function related to the attack probability is constructed to analyze the impact of malicious attacks. Sufficient conditions are derived to ensure that the proposed DTSMC law can drive the system dynamics onto the preset sliding region within a limited time.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Ting-Ting Cheng, Ben Niu, Jia-Ming Zhang, Ding Wang, Zhen-Hua Wang
Summary: This article proposes two adaptive asymptotic tracking control schemes for a class of interconnected systems with unmodeled dynamics and prescribed performance. The design difficulties caused by the unknown interactions among subsystems and unmodeled dynamics are overcome by utilizing the inherent property of radial basis function neural networks (NNs). The control problems are transformed into stabilization problems and a time-triggered controller is constructed based on the adaptive backstepping method. The effectiveness of the proposed control scheme is demonstrated through an illustrative example.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Fabin Cheng, Ben Niu, Ning Xu, Xudong Zhao, Adil M. Ahmad
Summary: This paper proposes a low-computation design scheme for fault detection and performance recovery based on deferred replacement actuators for a class of uncertain nonlinear systems. The proposed method does not require prior knowledge of fault models, nor does it require multiple actuators working in parallel to mitigate the impact of faults. It achieves performance recovery by designing fault detection and shifting functions, and establishes a computationally efficient design scheme.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Ben Niu, Bocheng Yan, Xudong Zhao, Baoyi Zhang, Tao Zhao, Xiaomei Liu
Summary: This paper investigates the event-triggered-based adaptive bipartite finite-time tracking control problem of nonlinear nonstrict-feedback coopetition multi-agent systems (MASs) with time-varying disturbances. The major design difficulties are solved by utilizing radial basis function neural networks and Gaussian functions. The proposed control approach successfully drives the tracking errors to the desired neighborhood of the origin in an almost fast finite time.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Haiyang Chen, Guangdeng Zong, Xiang Liu, Xudong Zhao, Ben Niu, Fangzheng Gao
Summary: This paper investigates the attack-compensated output control problem in Markov jump cyber-physical systems subject to mismatched modes. An adaptive probabilistic event-triggered mechanism is developed to enhance the control performance of the networked control system. A predictor-based compensator is constructed to mitigate the impact of attacks on the control performance. A mismatched output feedback controller is designed, and the stability analysis is performed. Simulations are conducted to validate the proposed results.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Automation & Control Systems
Xiaomei Wang, Jing Na, Ben Niu, Xudong Zhao, Tingting Cheng, Wenqi Zhou
Summary: This paper proposes an adaptive bipartite secure consensus asymptotic tracking control scheme based on event-triggered strategy for the nonlinear multi-agent systems (MASs) under denial-of-service (DoS) attacks. The paper successfully addresses the bipartite consensus control problem with unbalanced communication topology by incorporating the concept of shortest path into the hierarchical algorithm. An anti-attack bipartite control strategy is proposed using improved forms of tracking errors and virtual controllers, and a modified event-triggered mechanism based on relative threshold strategy ensures asymptotic convergence of bipartite consensus tracking errors.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)